artificial intelligence neuropathologist

Conditions: CNS Tumor; Neuropathology

Summary: cns tumor requires biopsy for pathological diagnosis, which is known as the "golden standard". we would like to achieve automated classification of brain tumors based on deep learning in digital histopathology images and molecular pathology results. we expect to develop an assistant system (including software and hardware), to help pathologists during their diagnosis for cns tumor.

the aim of the study is to develop an automated pathological diagnosis system for cns tumors based on deep learning technique. it is designed to firstly develop the best deep learning model for pathological diagnosis of cns tumors, in order to improve the accuracy of pathological diagnosis. then to be used clinically, reduce the workload and stress of neuropathologists and obtain the benefits for cns tumor patients. different cns tumors including meningioma, glioma, lymphoma and other various tumors have their own different treatment principles and plans. for example, high grade glioma requires operational resection and post-operational chemo-radiotherapy. however, operational resection is not significant for improving prognosis in lymphoma patients, systematic chemotherapy will be performed after specific diagnosis based on biopsy. therefore, in this study, an automated cns tumor pathological diagnosis system will be developed to classify the different type of those tumors. at present, pathological diagnosis of cns tumors is based on histopathological characteristics and molecular information after a systematic analyzed by pathologists. the accuracy of the diagnosis very much relies on the experience of the pathologists. however, to become a experienced and qualified pathologist requires years of training. pathologists may give completely different diagnose outcome for the same patient. thus, it is essential to develop a system that can assist pathologists. deep learning is one of the most advanced techniques of artificial intelligence. in particular, the ability of image recognition is extremely powerful. therefore, we are able to develop a model for histopathological section images based on deep learning. who classification of cns tumors 2016 has included molecular markers as the important part of diagnosis. hence, there will be an additional model of molecular pathology to be added to the system. huashan hospital has one of the largest cns tumor biobank in china, which is the key part for deep learning, as it needs large amount of data. the case load of this study is able to show the representative and authoritative of those data. there will be three stages of the study. stage 1 and 2 are supervised learning process. stage 1 is to develop the best deep learning model for histopathological diagnosis of cns tumors, we anticipate the accuracy for the first model to achieve at least 70%. the training data (pathological sections) will be provided by huashan hospital cns tumor biobank. in the mean time, a micro-positioning platform is under investigation for the use of image collection. at the end of stage 1, we anticipate to integrate the model (software) and the platform (hardware) as the whole diagnose system for histopathological images. stage 2 is to design a model for molecular pathological diagnosis for cns tumors. the model will be trained by numerous amount of related molecular information extracted from those pathological sections. at the end of stage 2, we anticipate to combine stage 1 system and stage 2 model as the primary prototype. stage 3 is known as the unsupervised learning process. by using the prototype developed after previous stages, the system will be used clinically. with the incoming of more patients and data, together with pathologists in the hospital, it will give its diagnosis. by comparing the results with pathologists, it will be able to self-learn and improve the accuracy as the time goes on. by the end of stage 3, we anticipate to have the system ready for independent clinical pathological diagnosis ability with the accuracy greater than 90%.

Eligibility criteria:
inclusion criteria: the participants diagnosed with brain cancer by diagnosis of who 2016 classification of cns tumors. exclusion criteria: voluntarily quit

Keywords: cns tumor, neuropathology, deep learning